Multi-step ahead prediction of hourly influent characteristics for wastewater treatment plants: a case study from North America.
High temporal resolution
Lead time
Machine-learning models
Multivariate analysis
Univariate analysis
Wastewater influent
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
21 Apr 2022
21 Apr 2022
Historique:
received:
08
11
2021
accepted:
12
03
2022
entrez:
21
4
2022
pubmed:
22
4
2022
medline:
26
4
2022
Statut:
epublish
Résumé
Prediction of influent characteristics, before any treatment takes place, is of great importance to the operation and management of wastewater treatment plants (WWTPs). In this study, four machine-learning models, including multilayer perceptron (MLP), long short-term memory network (LSTM), K-nearest neighbour (KNN), and random forest (RF), are introduced to utilize real-time wastewater data from three WWTPs in North America (i.e., Tres Rios, Woodward, and one confidential plant) for predicting hourly influent characteristics. Input variables are selected using an autocorrelation analysis and a variable importance measure from RF. Both univariate and multivariate analyses are investigated to improve model accuracy. The performances of one- and multiple-step-ahead models are compared. With a short prediction horizon, all the models derived from both univariate and multivariate analyses show excellent performance. It was found that the performance deterioration as the prediction horizon expands could be mitigated significantly by including extra variables, such as meteorological variables. This work can provide valuable support for the high-temporal-resolution prediction of wastewater influent characteristics for WWTPs. The proposed models can also bridge the gap between data and decision-making in the wastewater sector.
Identifiants
pubmed: 35445887
doi: 10.1007/s10661-022-09957-y
pii: 10.1007/s10661-022-09957-y
doi:
Substances chimiques
Waste Water
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
389Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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